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+ Update A = kernel.gen_pma_dma_grid(..l.) call in main_0 to take pre-defined prop_pma.
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tsipkens committed Mar 26, 2021
1 parent df15526 commit 96eb9cb
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8 changes: 4 additions & 4 deletions README.md
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Expand Up @@ -113,11 +113,11 @@ grid_x.plot2d(x0); % show the phantom in figure 1
<img width="420" src="docs/01a_distr4.png">
</p>

Here the vertical axis corresponds to the mass in fg that we specified at the beginning, and the horizontal axis to the mobility diameter in nm. Note that we chose a very narrow phantom. The white lines indicate the edges of the partial grid that we defined in a previous step.
Here the vertical axis corresponds to the mass in fg that we specified at the beginning, and the horizontal axis to the mobility diameter in nm. Note that we chose a very narrow phantom. The white lines indicate the edges of the partial grid that we defined in a previous step. This is what we will try to reconstruct.

### (2) Compute the kernel / transfer functions

Next, we need to compute the kernel. This first requires us to define the setpoints on which the data will be generated. Here, we defined a new `Grid` for the points at which the measurements will take place:
Next, we need to compute the kernel (or device transfer functions). This first requires us to define the setpoints on which the data will be generated. Here, we defined a new `Grid` for the points at which the measurements will take place:

```Matlab
% Define a new grid for the measurements.
Expand All @@ -137,8 +137,8 @@ prop_pma = kernel.prop_pma
Then, since we have a grid for the mass-mobility distribution and the data, use the `kernel.gen_pma_dma_grid(...)` method:

```Matlab
% Generate the kernel, use default CPMA properties.
A = kernel.gen_pma_dma_grid(grid_b, grid_x);
% Generate the kernel, use the above CPMA properties.
A = kernel.gen_pma_dma_grid(grid_b, grid_x, prop_pma);
```

One can visualize the two-dimensional kernel for the 530<sup>th</sup> data point using:
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2 changes: 1 addition & 1 deletion main_0.m
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Expand Up @@ -46,7 +46,7 @@
prop_pma = kernel.prop_pma;

% Generate the kernel, use default CPMA properties.
A = kernel.gen_pma_dma_grid(grid_b, grid_x);
A = kernel.gen_pma_dma_grid(grid_b, grid_x, prop_pma);

figure(2);
grid_x.plot2d_marg(A(527,:)); % plot kernel for 527th data point
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